Currently, Machine learning and Data Science are the most in-demand technologies of this era. This massive surge in demand has forced programmers to learn various Python libraries and packages in order to implement data science and machine learning.
This is why in this blog post, we will focus on various Python libraries for machine learning and data science. Here are some of the libraries you should know to become a crackerjack of Python programming.
Why Use Python for Data Science and Machine Learning?
Nowadays, Python ranks among the most popular programming languages used to implement machine learning and data science. Let’s spill the beans on why so many data scientists and machine learning experts prefer Python over other programming languages.
Ease of learning: Python utilizes a straightforward syntax that anyone can use to implement simple calculations, such as adding two strings to complex calculations or creating various machine learning models.
Less code: Implementing machine learning and data science needs a plethora of algorithms. Due to Python’s support for predefined packages, we don’t require coding algorithms. For making it effortless, Python offers a “proof-while-coding” methodology that minimizes the code testing’s burden.
Prebuilt Libraries: Python has numerous pre-built libraries that allow it to implement different machine learning and deep learning algorithms. So, whenever you want to run any algorithm on a dataset, all you need is to install the required packages with one command. Examples of ready-made libraries include NumPy, Keras, Tensorflow, Pytorch, and so on.
List of Python Libraries for Data Science and Machine Learning
Here is the line up of the most important libraries for data science and Machine Learning in Python such as Data Visualization, Data Mining and Data modeling.
- NumPy
- Scrapy
- BeautifulSoup
- Pandas
- Scikit-Learn
- PyTorch
- TensorFlow
- Matplotlib
- SciPy
- Keras
- XGBoost
- pydot
- Plotly
- Bokeh
- Seaborn
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